class_docstr: Wrapping network def for a specified return_to_condition.
code: def __init__(self, network_def, min_return, max_return):
      self.network_def = network_def
      self._min_return = min_return
      self._max_return = max_return
      self.set_return_to_condition()
  
imports: import functools
  from absl import logging
  from dopamine.jax import losses
  from dopamine.jax.agents.dqn import dqn_agent
  from dopamine.jax.agents.rainbow import rainbow_agent
  from dopamine.labs.offline_rl.jax import offline_rainbow_agent
  import gin
  import jax
  import jax.numpy
  import numpy
  import optax
  import tensorflow
label_desc: Use image node_id 1 to create a new WrappedNetworkDef object from inherited base classes: object with example: obj = WrappedNetworkDef(network_def, min_return, max_return)